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Harvard University Postdoctoral Research Fellow for Statistical Methods in Population Health Disparities in Cambridge, Massachusetts

Title Postdoctoral Research Fellow for Statistical Methods in Population Health Disparities

School Harvard T.H. Chan School of Public Health

Department/Area Biostatistics

Position Description

The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a Postdoctoral Research Fellowship focused on the development of statistical methods for population health disparities. The postdoctoral fellow will work with Dr. Briana Stephenson and collaborate with a multidisciplinary research team to develop innovative statistical and machine learning methods to address and identify bias and inequities in population health. Areas of interest include: identifying bias in healthcare access and delivery, statistical methods for high-dimensional exposures in minority populations, model-based clustering techniques for understudied populations, and survey sampling methodology for diverse population cohorts. Research applications will utilize data from cancer registries, national survey studies, and large prospective cohort studies. The postdoctoral fellow will develop their research and training agendas through formal mentorship, seminars, conferences, and an Individual Development Plan ( IDP ) to explore and identify the fellow’s professional needs and career objectives.

Basic Qualifications

• Doctoral degree in Biostatistics, Applied Statistics, Computer Science, data science or related field

• Experience developing and implementing statistical methods

• Experience analyzing healthcare or population cohort study data

• Strong statistical programming skills (e.g. R, MATLAB , Python, C++, etc.)

• Strong oral and written communication skills

Additional Qualifications

• Experience implementing Bayesian models

• Experience processing and analyzing large datasets

Special Instructions

• Cover letter

• Curriculum vitae

• One-page research statement and/or one representative first author publication

• Two references

Contact Information

Susan Luvisi

Contact Email

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 2

Maximum Number of References Allowed 4

Supplemental Questions